survey

A Survey of Controllable Text Generation Using Transformer-based Pre-trained Language Models

Authors:

Dawei SongAuthors Info & Claims

ACM Computing Surveys, Volume 56, Issue 3

Article No.: 64, Pages 1 - 37

https://doi.org/10.1145/3617680

Published: 06 October 2023 Publication History

Abstract

Controllable Text Generation (CTG) is an emerging area in the field of natural language generation (NLG). It is regarded as crucial for the development of advanced text generation technologies that better meet the specific constraints in practical applications. In recent years, methods using large-scale pre-trained language models (PLMs), in particular the widely used Transformer-based PLMs, have become a new paradigm of NLG, allowing generation of more diverse and fluent text. However, due to the limited level of interpretability of deep neural networks, the controllability of these methods needs to be guaranteed. To this end, controllable text generation using Transformer-based PLMs has become a rapidly growing yet challenging new research hotspot. A diverse range of approaches have emerged in the past 3 to 4 years, targeting different CTG tasks that require different types of controlled constraints. In this article, we present a systematic critical review on the common tasks, main approaches, and evaluation methods in this area. Finally, we discuss the challenges that the field is facing, and put forward various promising future directions. To the best of our knowledge, this is the first survey article to summarize the state-of-the-art CTG techniques from the perspective of Transformer-based PLMs. We hope it can help researchers and practitioners in the related fields to quickly track the academic and technological frontier, providing them with a landscape of the area and a roadmap for future research.

References

[1]

Ali Amin-Nejad, Julia Ive, and Sumithra Velupillai. 2020. Exploring Transformer text generation for medical dataset augmentation. In Proceedings of the 12th Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, 4699–4708. https://aclanthology.org/2020.lrec-1.578

[2]

Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. 2017. Guided open vocabulary image captioning with constrained beam search. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, September 9–11, 2017, Martha Palmer, Rebecca Hwa, and Sebastian Riedel (Eds.). Association for Computational Linguistics, 936–945. DOI:

[3]

Wilker Aziz, Sheila Castilho, and Lucia Specia. 2012. PET: A tool for post-editing and assessing machine translation. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC’12). European Language Resources Association (ELRA), Istanbul, Turkey, 3982–3987. http://www.lrec-conf.org/proceedings/lrec2012/pdf/985_Paper.pdf

[4]

Soumya Barikeri, Anne Lauscher, Ivan Vulić, and Goran Glavaš. 2021. RedditBias: A real-world resource for bias evaluation and debiasing of conversational language models. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, Online, 1941–1955. DOI:

[5]

Iz Beltagy, Matthew E. Peters, and Arman Cohan. 2020. Longformer: The long-document Transformer. CoRR abs/2004.05150 (2020). arxiv:2004.05150. https://arxiv.org/abs/2004.05150

[6]

Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. On the dangers of stochastic parrots: Can language models be too big?. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT’21) (Virtual Event, Canada). Association for Computing Machinery, New York, NY, USA, 610–623. DOI:

Digital Library

[7]

Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Janvin. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3, null (Mar2003), 1137–1155.

Digital Library

[8]

Sumanta Bhattacharyya, Amirmohammad Rooshenas, Subhajit Naskar, Simeng Sun, Mohit Iyyer, and Andrew McCallum. 2021. Energy-based reranking: Improving neural machine translation using energy-based models. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, Online, 4528–4537. DOI:

[9]

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 1877–1901. https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf

[10]

Fredrik Carlsson, Joey Öhman, Fangyu Liu, Severine Verlinden, Joakim Nivre, and Magnus Sahlgren. 2022. Fine-grained controllable text generation using non-residual prompting. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Dublin, Ireland, 6837–6857. DOI:

[11]

Thiago Castro Ferreira, Chris van der Lee, Emiel van Miltenburg, and Emiel Krahmer. 2019. Neural data-to-text generation: A comparison between pipeline and end-to-end architectures. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, 552–562. DOI:

[12]

Asli Celikyilmaz, Antoine Bosselut, Xiaodong He, and Yejin Choi. 2018. Deep communicating agents for abstractive summarization. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). Association for Computational Linguistics, New Orleans, Louisiana, 1662–1675. DOI:

[13]

Asli Celikyilmaz, Elizabeth Clark, and Jianfeng Gao. 2020. Evaluation of text generation: A survey. CoRR abs/2006.14799 (2020). arxiv:2006.14799. https://arxiv.org/abs/2006.14799

[14]

Alvin Chan, Yew-Soon Ong, Bill Pung, Aston Zhang, and Jie Fu. 2021. CoCon: A self-supervised approach for controlled text generation. In International Conference on Learning Representations. https://openreview.net/forum?id=VD_ozqvBy4W

[15]

Khyathi Chandu, Shrimai Prabhumoye, Ruslan Salakhutdinov, and Alan W. Black. 2019. “My way of telling a story”: Persona based grounded story generation. In Proceedings of the 2nd Workshop on Storytelling. Association for Computational Linguistics, Florence, Italy, 11–21. DOI:

[16]

Haw-Shiuan Chang, Jiaming Yuan, Mohit Iyyer, and Andrew McCallum. 2021. Changing the mind of Transformers for topically-controllable language generation. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. Association for Computational Linguistics, Online, 2601–2611. DOI:

[17]

Cheng-Kuan Chen, Zhufeng Pan, Ming-Yu Liu, and Min Sun. 2019. Unsupervised stylish image description generation via domain layer norm. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 8151–8158.

Digital Library

[18]

Huimin Chen, Xiaoyuan Yi, Maosong Sun, Wenhao Li, Cheng Yang, and Zhipeng Guo. 2019. Sentiment-controllable Chinese poetry generation. In Proceedings of the T28th International Joint Conference on Artificial Intelligence, IJCAI-19. International Joint Conferences on Artificial Intelligence Organization, 4925–4931. DOI:

[19]

Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2022. Palm: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311 (2022).

[20]

Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022. Scaling instruction-finetuned language models. arXiv preprint arXiv:2210.11416 (2022).

[21]

Alexis Conneau and Guillaume Lample. 2019. Cross-Lingual Language Model Pretraining. Curran Associates Inc., Red Hook, NY, USA. https://proceedings.neurips.cc/paper/2019/file/c04c19c2c2474dbf5f7ac4372c5b9af1-Paper.pdf

[22]

Daniel Dahlmeier, Chang Liu, and Hwee Tou Ng. 2011. TESLA at WMT 2011: Translation evaluation and tunable metric. In Proceedings of the 6th Workshop on Statistical Machine Translation. Association for Computational Linguistics, Edinburgh, Scotland, 78–84. https://aclanthology.org/W11-2106

[23]

Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc Le, and Ruslan Salakhutdinov. 2019. Transformer-XL: Attentive language models beyond a fixed-length context. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy, 2978–2988. DOI:

[24]

Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, and Rosanne Liu. 2020. Plug and play language models: A simple approach to controlled text generation. In International Conference on Learning Representations. https://openreview.net/forum?id=H1edEyBKDS

[25]

Yuntian Deng, Anton Bakhtin, Myle Ott, Arthur Szlam, and Marc’Aurelio Ranzato. 2020. Residual energy-based models for text generation. In International Conference on Learning Representations. https://openreview.net/forum?id=B1l4SgHKDH

[26]

Michael Denkowski, Chris Dyer, and Alon Lavie. 2014. Learning from post-editing: Online model adaptation for statistical machine translation. In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, Gothenburg, Sweden, 395–404. DOI:

[27]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, 4171–4186. DOI:

[28]

Emily Dinan, Angela Fan, Adina Williams, Jack Urbanek, Douwe Kiela, and Jason Weston. 2020. Queens are powerful too: Mitigating gender bias in dialogue generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, 8173–8188. DOI:

[29]

Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019. Unified Language Model Pre-Training for Natural Language Understanding and Generation. Curran Associates Inc., Red Hook, NY, USA.

Digital Library

[30]

Yihe Dong, Jean-Baptiste Cordonnier, and Andreas Loukas. 2021. Attention is not all you need: Pure attention loses rank doubly exponentially with depth. CoRR abs/2103.03404 (2021). arXiv:2103.03404 https://arxiv.org/abs/2103.03404

[31]

Xinya Du, Junru Shao, and Claire Cardie. 2017. Learning to ask: Neural question generation for reading comprehension. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Vancouver, Canada, 1342–1352. DOI:

[32]

Angela Fan, Mike Lewis, and Yann Dauphin. 2018. Hierarchical neural story generation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Melbourne, Australia, 889–898. DOI:

[33]

Le Fang, Tao Zeng, Chaochun Liu, Liefeng Bo, Wen Dong, and Changyou Chen. 2021. Outline to story: Fine-grained controllable story generation from cascaded events. arXiv preprint arXiv:2101.00822 (2021). https://arxiv.org/pdf/2101.00822v1.pdf

[34]

Jessica Ficler and Yoav Goldberg. 2017. Controlling linguistic style aspects in neural language generation. In Proceedings of the Workshop on Stylistic Variation. Association for Computational Linguistics, Copenhagen, Denmark, 94–104. DOI:

[35]

Mauajama Firdaus, Hardik Chauhan, Asif Ekbal, and Pushpak Bhattacharyya. 2022. EmoSen: Generating sentiment and emotion controlled responses in a multimodal dialogue system. IEEE Transactions on Affective Computing 13, 3 (2022), 1555–1566. DOI:

[36]

Mauajama Firdaus, Arunav Shandilya, Asif Ekbal, and Pushpak Bhattacharyya. 2022. Being polite: Modeling politeness variation in a personalized dialog agent. IEEE Transactions on Computational Social Systems (2022), 1–10. DOI:

[37]

Tianyu Gao, Adam Fisch, and Danqi Chen. 2021. Making pre-trained language models better few-shot learners. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, Online, 3816–3830. DOI:

[38]

Xiang Gao, Yizhe Zhang, Sungjin Lee, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2019. Structuring latent spaces for stylized response generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, 1814–1823. DOI:

[39]

Dimitra Gkatzia and Saad Mahamood. 2015. A snapshot of NLG evaluation practices 2005–2014. In Proceedings of the 15th European Workshop on Natural Language Generation (ENLG). Association for Computational Linguistics, Brighton, UK, 57–60. DOI:

[40]

Seraphina Goldfarb-Tarrant, Tuhin Chakrabarty, Ralph Weischedel, and Nanyun Peng. 2020. Content planning for neural story generation with Aristotelian rescoring. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, 4319–4338. DOI:

[41]

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2020. Generative adversarial networks. Commun. ACM 63, 11 (2020), 139–144.

Digital Library

[42]

Aditya Grover, Jiaming Song, Alekh Agarwal, Kenneth Tran, Ashish Kapoor, Eric Horvitz, and Stefano Ermon. 2019. Bias Correction of Learned Generative Models Using Likelihood-Free Importance Weighting. Curran Associates Inc., Red Hook, NY, USA. https://papers.nips.cc/paper/2019/file/d76d8deea9c19cc9aaf2237d2bf2f785-Paper.pdf

[43]

Tatsunori B. Hashimoto, Hugh Zhang, and Percy Liang. 2019. Unifying human and statistical evaluation for natural language generation. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, 1689–1701. DOI:

[44]

Junxian He, Xinyi Wang, Graham Neubig, and Taylor Berg-Kirkpatrick. 2020. A probabilistic formulation of unsupervised text style transfer. ArXiv abs/2002.03912 (2020).

[45]

Xingwei He. 2021. Parallel refinements for lexically constrained text generation with BART. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, 8653–8666. DOI:

[46]

Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020. The curious case of neural text degeneration. In International Conference on Learning Representations. https://openreview.net/forum?id=rygGQyrFvH

[47]

Helmut Horacek. 2001. Building natural language generation systems - Ehud Reiter and Robert Dale (Eds.), University of Aberdeen and Macquarie University, Cambridge University Press, 2000, ISBN 0-521-62036-8. Artif. Intell. Medicine 22, 3 (2001), 277–280. DOI:

Digital Library

[48]

Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P. Xing. 2017. Controllable text generation. CoRR abs/1703.00955 (2017). arxiv:1703.00955. http://arxiv.org/abs/1703.00955

[49]

Xinyu Hua and Lu Wang. 2020. PAIR: Planning and iterative refinement in pre-trained Transformers for long text generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, 781–793. DOI:

[50]

Minlie Huang, Xiaoyan Zhu, and Jianfeng Gao. 2020. Challenges in building intelligent open-domain dialog systems. ACM Transactions on Information Systems (TOIS) 38, 3 (2020), 1–32.

Digital Library

[51]

Po-Sen Huang, Huan Zhang, Ray Jiang, Robert Stanforth, Johannes Welbl, Jack Rae, Vishal Maini, Dani Yogatama, and Pushmeet Kohli. 2020. Reducing sentiment bias in language models via counterfactual evaluation. In Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, Online, 65–83. DOI:

[52]

Panagiotis G. Ipeirotis, Foster Provost, and Jing Wang. 2010. Quality management on Amazon Mechanical Turk. In Proceedings of the ACM SIGKDD Workshop on Human Computation (HCOMP’10) (Washington DC). Association for Computing Machinery, New York, NY, USA, 64–67. DOI:

Digital Library

[53]

Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023. Survey of hallucination in natural language generation. ACM Comput. Surv. 55, 12, Article 248 (mar2023), 38 pages. DOI:

Digital Library

[54]

Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig. 2020. How can we know what language models know? Transactions of the Association for Computational Linguistics 8 (2020), 423–438. DOI:

[55]

Mihir Kale and Abhinav Rastogi. 2020. Template guided text generation for task-oriented dialogue. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, 6505–6520. DOI:

[56]

Mihir Kale and Abhinav Rastogi. 2020. Text-to-text pre-training for data-to-text tasks. In Proceedings of the 13th International Conference on Natural Language Generation. Association for Computational Linguistics, Dublin, Ireland, 97–102. https://aclanthology.org/2020.inlg-1.14

[57]

Pei Ke, Hao Zhou, Yankai Lin, Peng Li, Jie Zhou, Xiaoyan Zhu, and Minlie Huang. 2022. CTRLEval: An unsupervised reference-free metric for evaluating controlled text generation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Dublin, Ireland, 2306–2319. DOI:

[58]

Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong, and Richard Socher. 2019. CTRL: A conditional Transformer language model for controllable generation. CoRR abs/1909.05858 (2019). arxiv:1909.05858. http://arxiv.org/abs/1909.05858

[59]

Muhammad Khalifa, Hady Elsahar, and Marc Dymetman. 2021. A distributional approach to controlled text generation. In International Conference on Learning Representations. https://openreview.net/forum?id=jWkw45-9AbL

[60]

Diederik P. Kingma and Max Welling. 2013. Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013).

[61]

Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya. 2020. Reformer: The efficient Transformer. In International Conference on Learning Representations. https://openreview.net/forum?id=rkgNKkHtvB

[62]

Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, Nitish Shirish Keskar, Shafiq Joty, Richard Socher, and Nazneen Fatema Rajani. 2021. GeDi: Generative discriminator guided sequence generation. In Findings of the Association for Computational Linguistics: EMNLP 2021. Association for Computational Linguistics, Punta Cana, Dominican Republic, 4929–4952. DOI:

[63]

Brian Lester, Rami Al-Rfou, and Noah Constant. 2021. The power of scale for parameter-efficient prompt tuning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, 3045–3059. DOI:

[64]

Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016. A diversity-promoting objective function for neural conversation models. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, San Diego, California, 110–119. DOI:

[65]

Jiwei Li, Michel Galley, Chris Brockett, Georgios Spithourakis, Jianfeng Gao, and Bill Dolan. 2016. A persona-based neural conversation model. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Berlin, Germany, 994–1003. DOI:

[66]

Piji Li, Haisong Zhang, Xiaojiang Liu, and Shuming Shi. 2020. Rigid formats controlled text generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel R. Tetreault (Eds.). Association for Computational Linguistics, 742–751. DOI:

[67]

Shifeng Li, Shi Feng, Daling Wang, Kaisong Song, Yifei Zhang, and Weichao Wang. 2020. EmoElicitor: An open domain response generation model with user emotional reaction awareness. In Proceedings of the 9th International Joint Conference on Artificial Intelligence, IJCAI-20, Christian Bessiere (Ed.). International Joint Conferences on Artificial Intelligence Organization, 3637–3643. DOI:. Main track.

[68]

Xiang Lisa Li and Percy Liang. 2021. Prefix-tuning: Optimizing continuous prompts for generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, Online, 4582–4597. DOI:

[69]

Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, and Tatsunori Hashimoto. 2022. Diffusion-LM improves controllable text generation. ArXiv abs/2205.14217 (2022). https://arxiv.org/abs/2205.14217

[70]

Yi Liao, Yasheng Wang, Qun Liu, and Xin Jiang. 2019. GPT-based generation for classical Chinese poetry. arXiv preprint arXiv:1907.00151 (2019). https://arxiv.org/abs/1907.00151

[71]

Chin-Yew Lin. 2004. ROUGE: A package for automatic evaluation of summaries. In Text Summarization Branches Out. Association for Computational Linguistics, Barcelona, Spain, 74–81. https://aclanthology.org/W04-1013

[72]

Zhaojiang Lin, Andrea Madotto, Yejin Bang, and Pascale Fung. 2021. The adapter-bot: All-in-one controllable conversational model. In 35th AAAI Conference on Artificial Intelligence, AAAI 2021, 33rd Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The 11th Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021. AAAI Press, 16081–16083. https://ojs.aaai.org/index.php/AAAI/article/view/18018

[73]

Zhiyu Lin and Mark O. Riedl. 2021. Plug-and-blend: A framework for plug-and-play controllable story generation with sketches. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, Vol. 17. 58–65. https://arxiv.org/abs/2104.04039

[74]

Alisa Liu, Maarten Sap, Ximing Lu, Swabha Swayamdipta, Chandra Bhagavatula, Noah A. Smith, and Yejin Choi. 2021. DExperts: Decoding-time controlled text generation with experts and anti-experts. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, Online, 6691–6706. DOI:

[75]

Chang Liu, Daniel Dahlmeier, and Hwee Tou Ng. 2010. TESLA: Translation evaluation of sentences with linear-programming-based analysis. In Proceedings of the Joint 5th Workshop on Statistical Machine Translation and MetricsMATR. Association for Computational Linguistics, Uppsala, Sweden, 354–359. https://aclanthology.org/W10-1754

[76]

Danyang Liu, Juntao Li, Meng-Hsuan Yu, Ziming Huang, Gongshen Liu, Dongyan Zhao, and Rui Yan. 2020. A character-centric neural model for automated story generation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 1725–1732. https://ojs.aaai.org//index.php/AAAI/article/view/5536

[77]

Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2021. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. CoRR abs/2107.13586 (2021). arXiv:2107.13586 https://arxiv.org/abs/2107.13586

[78]

Ruibo Liu, Chenyan Jia, Jason Wei, Guangxuan Xu, Lili Wang, and Soroush Vosoughi. 2021. Mitigating political bias in language models through reinforced calibration. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 14857–14866. https://arxiv.org/abs/2104.14795

[79]

Ruibo Liu, Guangxuan Xu, Chenyan Jia, Weicheng Ma, Lili Wang, and Soroush Vosoughi. 2020. Data boost: Text data augmentation through reinforcement learning guided conditional generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu (Eds.). Association for Computational Linguistics, 9031–9041. DOI:

[80]

Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, and Luke Zettlemoyer. 2020. Multilingual denoising pre-training for neural machine translation. Transactions of the Association for Computational Linguistics 8 (2020), 726–742. DOI:

[81]

Ye Liu, Wolfgang Maier, Wolfgang Minker, and Stefan Ultes. 2021. Naturalness evaluation of natural language generation in task-oriented dialogues using BERT. arXiv preprint arXiv:2109.02938 (2021).

[82]

Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A robustly optimized BERT pretraining approach. CoRR abs/1907.11692 (2019). arxiv:1907.11692. http://arxiv.org/abs/1907.11692

[83]

Lajanugen Logeswaran, Honglak Lee, and Samy Bengio. 2018. Content preserving text generation with attribute controls. In Advances in Neural Information Processing Systems, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.), Vol. 31. Curran Associates, Inc.https://proceedings.neurips.cc/paper/2018/file/7cf64379eb6f29a4d25c4b6a2df713e4-Paper.pdf

[84]

Ryan Lowe, Michael Noseworthy, Iulian Vlad Serban, Nicolas Angelard-Gontier, Yoshua Bengio, and Joelle Pineau. 2017. Towards an automatic Turing test: Learning to evaluate dialogue responses. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Vancouver, Canada, 1116–1126. DOI:

[85]

Fuli Luo, Damai Dai, Pengcheng Yang, Tianyu Liu, Baobao Chang, Zhifang Sui, and Xu Sun. 2019. Learning to control the fine-grained sentiment for story ending generation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy, 6020–6026. DOI:

[86]

Nikolaos Malandrakis, Minmin Shen, Anuj Goyal, Shuyang Gao, Abhishek Sethi, and Angeliki Metallinou. 2019. Controlled text generation for data augmentation in intelligent artificial agents. In Proceedings of the 3rd Workshop on Neural Generation and Translation. Association for Computational Linguistics, Hong Kong, 90–98. DOI:

[87]

Nitika Mathur, Timothy Baldwin, and Trevor Cohn. 2019. Putting evaluation in context: Contextual embeddings improve machine translation evaluation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy, 2799–2808. DOI:

[88]

Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013).

[89]

Fatemehsadat Mireshghallah, Kartik Goyal, and Taylor Berg-Kirkpatrick. 2022. Mix and match: Learning-free controllable text generation using energy language models. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Dublin, Ireland, 401–415. DOI:

[90]

Tanushree Mitra, C. J. Hutto, and Eric Gilbert. 2015. Comparing person- and process-centric strategies for obtaining quality data on Amazon Mechanical Turk. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI’15) (Seoul, Republic of Korea). Association for Computing Machinery, New York, NY, USA, 1345–1354. DOI:

Digital Library

[91]

Linyong Nan, Dragomir Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chiachun Hsieh, Xiangru Tang, Aadit Vyas, Neha Verma, Pranav Krishna, Yangxiaokang Liu, Nadia Irwanto, Jessica Pan, Faiaz Rahman, Ahmad Zaidi, Mutethia Mutuma, Yasin Tarabar, Ankit Gupta, Tao Yu, Yi Chern Tan, Xi Victoria Lin, Caiming Xiong, Richard Socher, and Nazneen Fatema Rajani. 2021. DART: Open-domain structured data record to text generation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Online, 432–447. DOI:

[92]

Tong Niu and Mohit Bansal. 2018. Polite dialogue generation without parallel data. Transactions of the Association for Computational Linguistics 6 (2018), 373–389. https://transacl.org/ojs/index.php/tacl/article/view/1424

[93]

Jekaterina Novikova, Ondřej Dušek, and Verena Rieser. 2018. RankME: Reliable human ratings for natural language generation. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). Association for Computational Linguistics, New Orleans, Louisiana, 72–78. DOI:

[94]

Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, and Ryan Lowe. 2022. Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (Eds.), Vol. 35. Curran Associates, Inc., 27730–27744. https://proceedings.neurips.cc/paper_files/paper/2022/file/b1efde53be364a73914f58805a001731-Paper-Conference.pdf

[95]

Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Philadelphia, Pennsylvania, USA, 311–318. DOI:

Digital Library

[96]

Damian Pascual, Beni Egressy, Clara Meister, Ryan Cotterell, and Roger Wattenhofer. 2021. A plug-and-play method for controlled text generation. In Findings of the Association for Computational Linguistics: EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 16-20 November, 2021, Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (Eds.). Association for Computational Linguistics, 3973–3997. https://aclanthology.org/2021.findings-emnlp.334

[97]

Baolin Peng, Chenguang Zhu, Chunyuan Li, Xiujun Li, Jinchao Li, Michael Zeng, and Jianfeng Gao. 2020. Few-shot natural language generation for task-oriented dialog. In Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, Online, 172–182. DOI:

[98]

Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). Association for Computational Linguistics, New Orleans, Louisiana, 2227–2237. DOI:

[99]

Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Yejin Choi, and Zaïd Harchaoui. 2021. MAUVE: Human-machine divergence curves for evaluating open-ended text generation. CoRR abs/2102.01454 (2021). arxiv:2102.01454. https://arxiv.org/abs/2102.01454

[100]

Shrimai Prabhumoye, Alan W. Black, and Ruslan Salakhutdinov. 2020. Exploring controllable text generation techniques. In Proceedings of the 28th International Conference on Computational Linguistics. International Committee on Computational Linguistics, Barcelona, Spain (Online), 1–14. DOI:

[101]

Reid Pryzant, Richard Diehl Martinez, Nathan Dass, Sadao Kurohashi, Dan Jurafsky, and Diyi Yang. 2020. Automatically neutralizing subjective bias in text. In Proceedings of the AAAI conference on artificial intelligence, Vol. 34. 480–489.

[102]

Ratish Puduppully, Li Dong, and Mirella Lapata. 2019. Data-to-text generation with content selection and planning. In Proceedings of the AAAI conference on artificial intelligence, Vol. 33. 6908–6915.

Digital Library

[103]

Jing Qian, Li Dong, Yelong Shen, Furu Wei, and Weizhu Chen. 2022. Controllable natural language generation with contrastive prefixes. In Findings of the Association for Computational Linguistics: ACL 2022. Association for Computational Linguistics, Dublin, Ireland, 2912–2924. DOI:

[104]

Yusu Qian, Urwa Muaz, Ben Zhang, and Jae Won Hyun. 2019. Reducing gender bias in word-level language models with a gender-equalizing loss function. arXiv preprint arXiv:1905.12801 (2019).

[105]

Lianhui Qin, Michel Galley, Chris Brockett, Xiaodong Liu, Xiang Gao, Bill Dolan, Yejin Choi, and Jianfeng Gao. 2019. Conversing by reading: Contentful neural conversation with on-demand machine reading. In Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, Anna Korhonen, David R. Traum, and Lluís Márquez (Eds.). Association for Computational Linguistics, 5427–5436. https://www.aclweb.org/anthology/P19-1539/

[106]

Lianhui Qin, Sean Welleck, Daniel Khashabi, and Yejin Choi. 2022. COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics. DOI:

[107]

Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. OpenAI blog 1, 8 (2019), 9.

[108]

Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text Transformer. Journal of Machine Learning Research 21, 140 (2020), 1–67. http://jmlr.org/papers/v21/20-074.html

[109]

Hannah Rashkin, David Reitter, Gaurav Singh Tomar, and Dipanjan Das. 2021. Increasing faithfulness in knowledge-grounded dialogue with controllable features. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, Online, 704–718.

[110]

Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence embeddings using Siamese BERT-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, 3982–3992. DOI:

[111]

Leonardo F. R. Ribeiro, Martin Schmitt, Hinrich Schütze, and Iryna Gurevych. 2021. Investigating pretrained language models for graph-to-text generation. In Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI. Association for Computational Linguistics, Online, 211–227. DOI:

[112]

Leonardo F. R. Ribeiro, Yue Zhang, and Iryna Gurevych. 2021. Structural adapters in pretrained language models for AMR-to-text generation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021, Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (Eds.). Association for Computational Linguistics, 4269–4282. https://aclanthology.org/2021.emnlp-main.351

[113]

Yu-Ping Ruan and Zhenhua Ling. 2021. Emotion-regularized conditional variational autoencoder for emotional response generation. IEEE Transactions on Affective Computing (2021). https://arxiv.org/abs/2104.08857

[114]

Punyajoy Saha, Kanishk Singh, Adarsh Kumar, Binny Mathew, and Animesh Mukherjee. 2022. CounterGeDi: A controllable approach to generate polite, detoxified and emotional counterspeech. In Proceedings of the 31st International Joint Conference on Artificial Intelligence, IJCAI-22, Lud De Raedt (Ed.). International Joint Conferences on Artificial Intelligence Organization, 5157–5163. DOI:. AI for Good.

[115]

Bidisha Samanta, Mohit Agarwal, and Niloy Ganguly. 2020. Fine-grained sentiment Controlled text generation. arXiv preprint arXiv:2006.09891 (2020). https://arxiv.org/abs/2006.09891

[116]

Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, et al. 2022. Bloom: A 176b-parameter open-access multilingual language model. arXiv preprint arXiv:2211.05100 (2022).

[117]

Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, and Jacopo Staiano. 2020. Discriminative adversarial search for abstractive summarization. In Proceedings of the 37th International Conference on Machine Learning (ICML’20). JMLR.org, Article 793, 10 pages. https://arxiv.org/abs/2002.10375

[118]

Thibault Sellam, Dipanjan Das, and Ankur Parikh. 2020. BLEURT: Learning robust metrics for text generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, 7881–7892. DOI:

[119]

Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. Controlling politeness in neural machine translation via side constraints. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, San Diego, California, 35–40. DOI:

[120]

Yizhan Shao, Tong Shao, Minghao Wang, Peng Wang, and Jie Gao. 2021. A sentiment and style controllable approach for chinese poetry generation. In CIKM’21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1-5, 2021, Gianluca Demartini, Guido Zuccon, J. Shane Culpepper, Zi Huang, and Hanghang Tong (Eds.). ACM, 4784–4788. DOI:

Digital Library

[121]

Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng. 2020. Towards controllable biases in language generation. arXiv preprint arXiv:2005.00268 (2020).

[122]

Zhonghao Sheng, Kaitao Song, Xu Tan, Yi Ren, Wei Ye, Shikun Zhang, and Tao Qin. 2021. SongMASS: Automatic song writing with pre-training and alignment constraint. In 35th AAAI Conference on Artificial Intelligence, AAAI 2021, 33rd Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The 11th Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021. AAAI Press, 13798–13805. https://ojs.aaai.org/index.php/AAAI/article/view/17626

[123]

Wenxian Shi, Hao Zhou, Ning Miao, and Lei Li. 2020. Dispersed exponential family mixture VAEs for interpretable text generation. In International Conference on Machine Learning. PMLR, 8840–8851. http://proceedings.mlr.press/v119/shi20f/shi20f.pdf

[124]

Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020. AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, 4222–4235. DOI:

[125]

Kihyuk Sohn, Xinchen Yan, and Honglak Lee. 2015. Learning structured output representation using deep conditional generative models. In Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2 (NIPS’15) (Montreal, Canada). MIT Press, Cambridge, MA, USA, 3483–3491.

Digital Library

[126]

Haoyu Song, Yan Wang, Kaiyan Zhang, Wei-Nan Zhang, and Ting Liu. 2021. BoB: BERT over BERT for training persona-based dialogue models from limited personalized data. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021 (Volume 1: Long Papers), Virtual Event, August 1-6, 2021, Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli (Eds.). Association for Computational Linguistics, 167–177. DOI:

[127]

Haoyu Song, Yan Wang, Weinan Zhang, Xiaojiang Liu, and Ting Liu. 2020. Generate, delete and rewrite: A three-stage framework for improving persona consistency of dialogue generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel R. Tetreault (Eds.). Association for Computational Linguistics, 5821–5831. DOI:

[128]

Linfeng Song, Ante Wang, Jinsong Su, Yue Zhang, Kun Xu, Yubin Ge, and Dong Yu. 2020. Structural information preserving for graph-to-text generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel R. Tetreault (Eds.). Association for Computational Linguistics, 7987–7998. DOI:

[129]

Zhenqiao Song, Xiaoqing Zheng, Lu Liu, Mu Xu, and Xuanjing Huang. 2019. Generating responses with a specific emotion in dialog. In Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28-August 2, 2019, Volume 1: Long Papers, Anna Korhonen, David R. Traum, and Lluís Màrquez (Eds.). Association for Computational Linguistics, 3685–3695. DOI:

[130]

Nisan Stiennon, Long Ouyang, Jeff Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul Christiano. 2020. Learning to summarize from human feedback. In Proceedings of the 34th International Conference on Neural Information Processing Systems (NIPS’20) (Vancouver, BC, Canada). Curran Associates Inc., Red Hook, NY, USA, Article 253, 14 pages. https://proceedings.neurips.cc/paper/2020/file/1f89885d556929e98d3ef9b86448f951-Paper.pdf

[131]

Yixuan Su, David Vandyke, Sihui Wang, Yimai Fang, and Nigel Collier. 2021. Plan-then-generate: Controlled data-to-text generation via planning. In Findings of the Association for Computational Linguistics: EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 16-20 November, 2021, Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (Eds.). Association for Computational Linguistics, 895–909. https://aclanthology.org/2021.findings-emnlp.76

[132]

Pradyumna Tambwekar, Murtaza Dhuliawala, Lara J. Martin, Animesh Mehta, Brent Harrison, and Mark O. Riedl. 2019. Controllable neural story plot generation via reward shaping. In Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI-19. International Joint Conferences on Artificial Intelligence Organization, 5982–5988. DOI:

[133]

Hongyin Tang, Miao Li, and Beihong Jin. 2019. A topic augmented text generation model: Joint learning of semantics and structural features. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, 5090–5099. DOI:

[134]

Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023. LLaMA: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023).

[135]

Lifu Tu, Richard Yuanzhe Pang, Sam Wiseman, and Kevin Gimpel. 2020. ENGINE: Energy-based inference networks for non-autoregressive machine translation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel R. Tetreault (Eds.). Association for Computational Linguistics, 2819–2826. DOI:

[136]

Chris van der Lee, Albert Gatt, Emiel van Miltenburg, and Emiel Krahmer. 2021. Human evaluation of automatically generated text: Current trends and best practice guidelines. Computer Speech & Language 67 (2021), 101151. DOI:

[137]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems. 5998–6008. https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf

[138]

Olga Vechtomova, Hareesh Bahuleyan, Amirpasha Ghabussi, and Vineet John. 2018. Generating lyrics with variational autoencoder and multi-modal artist embeddings. CoRR abs/1812.08318 (2018). arxiv:1812.08318. http://arxiv.org/abs/1812.08318

[139]

Dingmin Wang, Ziyao Chen, Wanwei He, Li Zhong, Yunzhe Tao, and Min Yang. 2021. A template-guided hybrid pointer network for knowledge-based task-oriented dialogue systems. In Proceedings of the 1st Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc 2021). Association for Computational Linguistics, Online, 18–28. DOI:

[140]

Ke Wang and Xiaojun Wan. 2018. SentiGAN: Generating sentimental texts via mixture adversarial networks. In Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI-18. International Joint Conferences on Artificial Intelligence Organization, 4446–4452. DOI:

[141]

Ruize Wang, Zhongyu Wei, Ying Cheng, Piji Li, Haijun Shan, Ji Zhang, Qi Zhang, and Xuanjing Huang. 2020. Keep it consistent: Topic-aware storytelling from an image stream via iterative multi-agent communication. In Proceedings of the 28th International Conference on Computational Linguistics. International Committee on Computational Linguistics, Barcelona, Spain (Online), 2250–2260. DOI:

[142]

Wenlin Wang, Zhe Gan, Hongteng Xu, Ruiyi Zhang, Guoyin Wang, Dinghan Shen, Changyou Chen, and Lawrence Carin. 2019. Topic-guided variational auto-encoder for text generation. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, 166–177. DOI:

[143]

Yufei Wang, Ian Wood, Stephen Wan, Mark Dras, and Mark Johnson. 2021. Mention flags (MF): Constraining Transformer-based text generators. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, Online, 103–113. DOI:

[144]

Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V. Le. 2022. Finetuned language models are zero-shot learners. In International Conference on Learning Representations. https://openreview.net/forum?id=gEZrGCozdqR

[145]

Wei Wei, Jiayi Liu, Xianling Mao, Guibing Guo, Feida Zhu, Pan Zhou, and Yuchong Hu. 2019. Emotion-aware chat machine: Automatic emotional response generation for human-like emotional interaction. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, November 3-7, 2019, Wenwu Zhu, Dacheng Tao, Xueqi Cheng, Peng Cui, Elke A. Rundensteiner, David Carmel, Qi He, and Jeffrey Xu Yu (Eds.). ACM, 1401–1410. DOI:

Digital Library

[146]

Sean Welleck, Ilia Kulikov, Stephen Roller, Emily Dinan, Kyunghyun Cho, and Jason Weston. 2019. Neural text generation with unlikelihood training. In International Conference on Learning Representations. https://arxiv.org/abs/1908.04319

[147]

Zeqiu Wu, Michel Galley, Chris Brockett, Yizhe Zhang, Xiang Gao, Chris Quirk, Rik Koncel-Kedziorski, Jianfeng Gao, Hannaneh Hajishirzi, Mari Ostendorf, and Bill Dolan. 2021. A controllable model of grounded response generation. In 35th AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021. AAAI Press, 14085–14093. https://ojs.aaai.org/index.php/AAAI/article/view/17658

[148]

Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma. 2022. An explanation of in-context learning as implicit Bayesian inference. In International Conference on Learning Representations. https://openreview.net/forum?id=RdJVFCHjUMI

[149]

Peng Xu, Yanshuai Cao, and Jackie Chi Kit Cheung. 2019. Unsupervised controllable text generation with global variation discovery and disentanglement. CoRR abs/1905.11975 (2019). arxiv:1905.11975. http://arxiv.org/abs/1905.11975

[150]

Peng Xu, Mostofa Patwary, Mohammad Shoeybi, Raul Puri, Pascale Fung, Anima Anandkumar, and Bryan Catanzaro. 2020. MEGATRON-CNTRL: Controllable story generation with external knowledge using large-scale language models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, 2831–2845. DOI:

[151]

Kevin Yang and Dan Klein. 2021. FUDGE: Controlled text generation with future discriminators. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Online, 3511–3535. DOI:

[152]

Kexin Yang, Dayiheng Liu, Wenqiang Lei, Baosong Yang, Mingfeng Xue, Boxing Chen, and Jun Xie. 2022. Tailor: A Prompt-Based Approach to Attribute-Based Controlled Text Generation.

[153]

Pengcheng Yang, Lei Li, Fuli Luo, Tianyu Liu, and Xu Sun. 2019. Enhancing topic-to-essay generation with external commonsense knowledge. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy, 2002–2012. DOI:

[154]

Xiaopeng Yang, Xiaowen Lin, Shunda Suo, and Ming Li. 2018. Generating thematic Chinese poetry using conditional variational autoencoders with hybrid decoders. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI’18) (Stockholm, Sweden). AAAI Press, 4539–4545. https://arxiv.org/abs/1711.07632

Digital Library

[155]

Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R. Salakhutdinov, and Quoc V. Le. 2019. XLNet: Generalized autoregressive pretraining for language understanding. In Advances in Neural Information Processing Systems, H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett (Eds.), Vol. 32. Curran Associates, Inc.https://proceedings.neurips.cc/paper/2019/file/dc6a7e655d7e5840e66733e9ee67cc69-Paper.pdf

[156]

Y. Zeldes, D. Padnos, O. Sharir, and B. Peleg. 2020. Technical report: Auxiliary tuning and its application to conditional text generation. (2020). https://arxiv.org/abs/2006.16823

[157]

Yan Zeng and Jian-Yun Nie. 2021. A simple and efficient multi-task learning approach for conditioned dialogue generation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021, Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tür, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, and Yichao Zhou (Eds.). Association for Computational Linguistics, 4927–4939. DOI:

[158]

Hanqing Zhang and Dawei Song. 2022. DisCup: Discriminator cooperative unlikelihood prompt-tuning for controllable text generation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates, 3392–3406. https://aclanthology.org/2022.emnlp-main.223

[159]

Rui Zhang, Zhenyu Wang, Kai Yin, and Zhenhua Huang. 2019. Emotional text generation based on cross-domain sentiment transfer. IEEE Access 7 (2019), 100081–100089. https://ieeexplore.ieee.org/document/8772090

[160]

Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, and Jason Weston. 2018. Personalizing dialogue agents: I have a dog, do you have pets too?. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Melbourne, Australia, 2204–2213. DOI:

[161]

Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020. BERTScore: Evaluating text generation with BERT. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net. https://openreview.net/forum?id=SkeHuCVFDr

[162]

Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, and Bill Dolan. 2020. DIALOGPT : Large-scale generative pre-training for conversational response generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. Association for Computational Linguistics, Online, 270–278. DOI:

[163]

Yizhe Zhang, Guoyin Wang, Chunyuan Li, Zhe Gan, Chris Brockett, and Bill Dolan. 2020. POINTER: Constrained progressive text generation via insertion-based generative pre-training. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, 8649–8670. DOI:

[164]

Zhengyan Zhang, Xu Han, Zhiyuan Liu, Xin Jiang, Maosong Sun, and Qun Liu. 2019. ERNIE: Enhanced language representation with informative entities. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy, 1441–1451. DOI:

[165]

Chao Zhao, Marilyn A. Walker, and Snigdha Chaturvedi. 2020. Bridging the structural gap between encoding and decoding for data-to-text generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel R. Tetreault (Eds.). Association for Computational Linguistics, 2481–2491. DOI:

[166]

Junbo Jake Zhao, Michaël Mathieu, and Yann LeCun. 2017. Energy-based generative adversarial networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net. https://openreview.net/forum?id=ryh9pmcee

[167]

Yinhe Zheng, Rongsheng Zhang, Minlie Huang, and Xiaoxi Mao. 2020. A pre-training based personalized dialogue generation model with persona-sparse data. In The 34th AAAI Conference on Artificial Intelligence, AAAI 2020, The 32nd Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The 10th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020. AAAI Press, 9693–9700. https://aaai.org/ojs/index.php/AAAI/article/view/6518

[168]

Peixiang Zhong, Chen Zhang, Hao Wang, Yong Liu, and Chunyan Miao. 2020. Towards persona-based empathetic conversational models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, 6556–6566. DOI:

[169]

Wangchunshu Zhou and Ke Xu. 2020. Learning to compare for better training and evaluation of open domain natural language generation models. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 9717–9724. https://arxiv.org/abs/2002.05058

[170]

Yaoming Zhu, Sidi Lu, Lei Zheng, Jiaxian Guo, Weinan Zhang, Jun Wang, and Yong Yu. 2018. Texygen: A benchmarking platform for text generation models. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR’18) (Ann Arbor, MI, USA). Association for Computing Machinery, New York, NY, USA, 1097–1100. DOI:

Digital Library

[171]

Daniel M. Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B. Brown, Alec Radford, Dario Amodei, Paul F. Christiano, and Geoffrey Irving. 2019. Fine-tuning language models from human preferences. CoRR abs/1909.08593 (2019). arxiv:1909.08593. http://arxiv.org/abs/1909.08593

[172]

Xu Zou, Da Yin, Qingyang Zhong, Hongxia Yang, Zhilin Yang, and Jie Tang. 2021. Controllable generation from pre-trained language models via inverse prompting. CoRR abs/2103.10685 (2021). arxiv:2103.10685. https://arxiv.org/abs/2103.10685

Cited By

Kang MJeon D(2024)Enhancing the Performance of Generative AI-Based Educational Material Recommendation Functions: Focusing on Query-Based Prompt EngineeringJournal of Digital Contents Society10.9728/dcs.2024.25.6.160125:6(1601-1609)Online publication date: 30-Jun-2024
https://doi.org/10.9728/dcs.2024.25.6.1601
Gu ZHe K(2024)Affective Prompt-Tuning-Based Language Model for Semantic-Based Emotional Text GenerationInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.33918720:1(1-19)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.4018/IJSWIS.339187
Sindiramutty SV. Prabagaran KJhanjhi NMurugesan RBrohi SWei G(2024)Generative AI for Threat Intelligence and Information SharingUtilizing Generative AI for Cyber Defense Strategies10.4018/979-8-3693-8944-7.ch006(191-234)Online publication date: 13-Sep-2024
https://doi.org/10.4018/979-8-3693-8944-7.ch006
Show More Cited By

Index Terms

A Survey of Controllable Text Generation Using Transformer-based Pre-trained Language Models
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

Recommendations

Pre-Trained Language Models for Text Generation: A Survey
Text Generation aims to produce plausible and readable text in human language from input data. The resurgence of deep learning has greatly advanced this field, in particular, with the help of neural generation models based on pre-trained language models (...
Wide-locking range LC-tank divide-by-4 injection-locked frequency divider using transformer feedback

A wide locking range, injection locked frequency divider ILFD circuit topology is explored. The modulus-four ILFD utilizes a cross-coupled voltage-controlled oscillator in conjunction with transformer feedback, parallel-tuned resonator, and two-segment, ...
A 190 GHz VCO with Transformer-Based Push---Push Frequency Doubler in 40 nm CMOS

A 190 GHz voltage-controlled oscillator (VCO) with transformer-based push---push frequency doubler in 40 nm CMOS is presented. The layout optimization reduces the parasitics of the transistors. To achieve high output power at a target operating ...

Comments

Information & Contributors

Information

Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 56, Issue 3

March 2024

977 pages

EISSN:1557-7341

DOI:10.1145/3613568

Editor:
Albert Zomaya
University of Sydney, Australia

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 October 2023

Online AM: 30 August 2023

Accepted: 21 August 2023

Revised: 14 July 2023

Received: 10 January 2022

Published in CSUR Volume 56, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Survey

Funding Sources

Natural Science Foundation of Beijing

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

47
Total Citations
View Citations
5,325
Total Downloads

Downloads (Last 12 months)5,305
Downloads (Last 6 weeks)340

Reflects downloads up to 23 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Kang MJeon D(2024)Enhancing the Performance of Generative AI-Based Educational Material Recommendation Functions: Focusing on Query-Based Prompt EngineeringJournal of Digital Contents Society10.9728/dcs.2024.25.6.160125:6(1601-1609)Online publication date: 30-Jun-2024
https://doi.org/10.9728/dcs.2024.25.6.1601
Gu ZHe K(2024)Affective Prompt-Tuning-Based Language Model for Semantic-Based Emotional Text GenerationInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.33918720:1(1-19)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.4018/IJSWIS.339187
Sindiramutty SV. Prabagaran KJhanjhi NMurugesan RBrohi SWei G(2024)Generative AI for Threat Intelligence and Information SharingUtilizing Generative AI for Cyber Defense Strategies10.4018/979-8-3693-8944-7.ch006(191-234)Online publication date: 13-Sep-2024
https://doi.org/10.4018/979-8-3693-8944-7.ch006
Pfeifer VChilton TGrilli MMehl M(2024)How ready is speech-to-text for psychological language research? Evaluating the validity of AI-generated English transcripts for analyzing free-spoken responses in younger and older adultsBehavior Research Methods10.3758/s13428-024-02440-156:7(7621-7631)Online publication date: 21-May-2024
https://doi.org/10.3758/s13428-024-02440-1
Chang TBergen B(2024)Language Model Behavior: A Comprehensive SurveyComputational Linguistics10.1162/coli_a_0049250:1(293-350)Online publication date: 1-Mar-2024
https://doi.org/10.1162/coli_a_00492
Liang PZadeh AMorency L(2024)Foundations & Trends in Multimodal Machine Learning: Principles, Challenges, and Open QuestionsACM Computing Surveys10.1145/365658056:10(1-42)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3656580
Huang SLin YHe ZHuang CHuang T(2024)How Does Conversation Length Impact User’s Satisfaction? A Case Study of Length-Controlled Conversations with LLM-Powered ChatbotsExtended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613905.3650823(1-13)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3650823
Zhou LQin YYan SZhang GHu W(2024)Application Analysis of Generative Artificial Intelligence in The field of Open Source Intelligence2024 39th Youth Academic Annual Conference of Chinese Association of Automation (YAC)10.1109/YAC63405.2024.10598766(1603-1608)Online publication date: 7-Jun-2024
https://doi.org/10.1109/YAC63405.2024.10598766
Li JYu ZDu ZZhu LShen H(2024)A Comprehensive Survey on Source-Free Domain AdaptationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.337097846:8(5743-5762)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TPAMI.2024.3370978
Pan SLuo LWang YChen CWang JWu X(2024)Unifying Large Language Models and Knowledge Graphs: A RoadmapIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.335210036:7(3580-3599)Online publication date: Jul-2024
https://doi.org/10.1109/TKDE.2024.3352100
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents